Management Accounting in the Big Data Era – Opportunities or Threats?

Publication Type:
Conference Proceeding
Citation: (nya), 2018
Issue Date:
Filename Description Size
PAPER ICESAL17 DS_MA no EN.pdfAccepted Manuscript version407.2 kB
Adobe PDF
Full metadata record
Over the past two decades, the digital revolution has brought along (a) ‘Big Data’, i.e. data which have rapidly become too big in volume, too diverse in nature and too rapidly changing to be handled in conventional databases and analysed using conventional tools, and (b) ‘data science’, “the study of the generalizable extraction of knowledge from data” (Dhar 2013), which develops and applies tools to manage and analyse (Big) Data. Data scientists are seen as new breed of managerial decision supporters, and insofar cross traditional management accounting territory. The aim of this study is to investigate the current and predict the future relationships between management accounting and the emerging data science discipline, based on a systematic analysis of the academic and practitioner literatures. While there is very little empirical evidence of an actual impact of data science on the management accounting profession, such impacts are predicted for the near future. Management accountants are expected to break with their traditions and collaborate with data scientists for mutual benefits. On the one hand, management accountants can be ‘data businesspeople’ or ‘horizontal data scientists’, who contribute essential business knowledge and data understanding to data science/Big Data projects. To succeed in such efforts, established and graduating management accountants face a need for up-skilling in technology, statistics, data mining, etc. and move into deeper analysis. Data scientists, on the other hand, can use their technical expertise to enrich established management accounting techniques and practices (e.g. the Balanced Scorecard, forecasting, etc.) with more advanced statistical or machine learning techniques.
Please use this identifier to cite or link to this item: